Análisis de modelos SOM - Frecuencia datos de entrada: mes

Modelo

• ID: 313
• Descripción:
• Frecuencia: mes
• Variables: precip, longitud, latitud, altitud
• Dimensiones del mapa: 5,5
• Iteraciones: 1000
• Parámetros adicionales:

source("../../lib/som-utils.R")

Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

source("../../lib/maps-utils.R")

Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 6.3.1

Carga del modelo desde disco

mpr.set_base_path_analysis()
model <- mpr.load_model("som-313.rds.xz")
summary(model)

SOM of size 5x5 with a hexagonal topology and a bubble neighbourhood function.
The number of data layers is 1.
Distance measure(s) used: sumofsquares.
Training data included: 94881 objects.
Mean distance to the closest unit in the map: 0.367.

plot(model, type="changes")

Carga del dataset de entrada

df <- mpr.load_data("datos_mes.csv.xz")

df

summary(df)

 id_estacion           fecha             fecha_cnt           tmax      
 Length:94881       Length:94881       Min.   : 1.000   Min.   :-53.0  
 Class :character   Class :character   1st Qu.: 4.000   1st Qu.:148.0  
 Mode  :character   Mode  :character   Median : 6.000   Median :198.0  
                                       Mean   : 6.497   Mean   :200.2  
                                       3rd Qu.: 9.000   3rd Qu.:255.0  
                                       Max.   :12.000   Max.   :403.0  
      tmin             precip           nevada           prof_nieve      
 Min.   :-121.00   Min.   :  0.00   Min.   :0.000000   Min.   :   0.000  
 1st Qu.:  53.00   1st Qu.:  3.00   1st Qu.:0.000000   1st Qu.:   0.000  
 Median :  98.00   Median : 10.00   Median :0.000000   Median :   0.000  
 Mean   :  98.86   Mean   : 16.25   Mean   :0.000295   Mean   :   0.467  
 3rd Qu.: 148.00   3rd Qu.: 22.00   3rd Qu.:0.000000   3rd Qu.:   0.000  
 Max.   : 254.00   Max.   :422.00   Max.   :6.000000   Max.   :1834.000  
    longitud        latitud            altitud      
 Min.   :27.82   Min.   :-17.8889   Min.   :   1.0  
 1st Qu.:38.28   1st Qu.: -5.6417   1st Qu.:  42.0  
 Median :40.82   Median : -3.4500   Median : 247.0  
 Mean   :39.66   Mean   : -3.4350   Mean   : 418.5  
 3rd Qu.:42.08   3rd Qu.:  0.4914   3rd Qu.: 656.0  
 Max.   :43.57   Max.   :  4.2156   Max.   :2535.0  

Carga de los mapas

world <- ne_countries(scale = "medium", returnclass = "sf")
spain <- subset(world, admin == "Spain")

Mapa de densidad

plot(model, type="count", shape = "straight", palette.name = mpr.degrade.bleu)

Número de elementos en cada celda:

nb <- table(model$unit.classif)
print(nb)

   1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16 
5650 8387 8522 8402 1981 1190 3107 3207 4030 4740  949 3526 5637 4720 1852   87 
  17   18   19   20   21   22   23   24   25 
 739 2617 5677 5211 1364 3683 4036 2975 2592 

Comprobación de nodos vacíos:

dim_model <- 5*5;
len_nb = length(nb);
empty_nodes <- dim_model != len_nb;
if (empty_nodes) {
  print(paste("[Warning] Existen nodos vacíos: ", len_nb, "/", dim_model))
}

Mapa de distancia entre vecinos

plot(model, type="dist.neighbours", shape = "straight")

Influencia de las variables

model_colnames = c("precip", "longitud", "latitud", "altitud")
model_ncol = length(model_colnames)

Mapa de variables.

plot(model, shape = "straight")

Mapa de calor por variable

par(mfrow=c(3,4))
for (j in 1:model_ncol) {
  plot(model, type="property", property=getCodes(model,1)[,j],
    palette.name=mpr.coolBlueHotRed,
    main=model_colnames[j],
    cex=0.5, shape = "straight")
}

Correlación para cada columna del vector de nodos

if (!empty_nodes) {
  cor <- apply(getCodes(model,1), 2, mpr.weighted.correlation, w=nb, som=model)
  print(cor)
}

          precip  longitud     latitud   altitud
[1,] -0.04032153 0.6465287  0.73799492 0.2886422
[2,]  0.36501065 0.5450317 -0.06942733 0.4845284

Representación de cada variable en un mapa de factores:

if (!empty_nodes) {
  par(mfrow=c(1,1))
  plot(cor[1,], cor[2,], xlim=c(-1,1), ylim=c(-1,1), type="n")
  lines(c(-1,1),c(0,0))
  lines(c(0,0),c(-1,1))
  text(cor[1,], cor[2,], labels=model_colnames, cex=0.75)
  symbols(0,0,circles=1,inches=F,add=T)
}

Importancia de cada variable - varianza ponderada por el tamaño de la celda:

if (!empty_nodes) {
  sigma2 <- sqrt(apply(getCodes(model,1),2,function(x,effectif)
     {m<-sum(effectif*(x-weighted.mean(x,effectif))^2)/(sum(effectif)-1)},
     effectif=nb))
  print(sort(sigma2,decreasing=T))
}

 longitud   latitud   altitud    precip 
0.9768367 0.9609975 0.9567491 0.9142196 

Clustering

if (!empty_nodes) {
  hac <- mpr.hac(model, nb)
}

Visualización de 3 clústeres:

if (!empty_nodes) {
  plot(hac, hang=-1, labels=F)
  rect.hclust(hac, k=3)
}

Visualización de los clústers en el mapa

A qué clúster pertenece cada nodo del mapa de kohonen:

if (!empty_nodes) {
  groups <- cutree(hac, k=3)
  plot(model, type="mapping",
    bgcol=c("steelblue1","sienna1","yellowgreen","red","blue","yellow","purple","green","white","#1f77b4", '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2')[groups],
    shape = "straight", labels = "")
  add.cluster.boundaries(model, clustering=groups)
}

Análisis de las observaciones de cada cluster

if (!empty_nodes) {
  # Asignamos a cada registro su clúster
  df$cluster <- groups[model$unit.classif]
}

Nuevos dataframes por cluster

if (!empty_nodes) {
  # Creo nuevos dataframes, uno por cada clúster.
  df.cluster01 <- subset(df, cluster==1)
  df.cluster02 <- subset(df, cluster==2)
  df.cluster03 <- subset(df, cluster==3)

  # Extraigo del dataframe las features.
  df.cluster01 <- select(df.cluster01, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster02 <- select(df.cluster02, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster03 <- select(df.cluster03, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
}

if (!empty_nodes) summary(df.cluster01)

   fecha_cnt           tmax            tmin           precip            nevada 
 Min.   : 1.000   Min.   : 13.0   Min.   :-33.0   Min.   :  0.000   Min.   :0  
 1st Qu.: 4.000   1st Qu.:201.0   1st Qu.:132.0   1st Qu.:  0.000   1st Qu.:0  
 Median : 6.000   Median :225.0   Median :160.0   Median :  1.000   Median :0  
 Mean   : 6.497   Mean   :217.5   Mean   :150.7   Mean   :  6.631   Mean   :0  
 3rd Qu.: 9.000   3rd Qu.:253.0   3rd Qu.:190.0   3rd Qu.:  8.000   3rd Qu.:0  
 Max.   :12.000   Max.   :356.0   Max.   :244.0   Max.   :130.000   Max.   :0  
   prof_nieve          longitud        latitud          altitud      
 Min.   : 0.00000   Min.   :27.82   Min.   :-17.89   Min.   :  14.0  
 1st Qu.: 0.00000   1st Qu.:28.31   1st Qu.:-16.50   1st Qu.:  25.0  
 Median : 0.00000   Median :28.44   Median :-16.33   Median :  35.0  
 Mean   : 0.03099   Mean   :28.36   Mean   :-16.05   Mean   : 519.8  
 3rd Qu.: 0.00000   3rd Qu.:28.48   3rd Qu.:-15.39   3rd Qu.: 632.0  
 Max.   :46.00000   Max.   :28.95   Max.   :-13.60   Max.   :2371.0  

if (!empty_nodes) summary(df.cluster02)

   fecha_cnt           tmax            tmin             precip      
 Min.   : 1.000   Min.   :-53.0   Min.   :-121.00   Min.   :  0.00  
 1st Qu.: 4.000   1st Qu.:146.0   1st Qu.:  50.00   1st Qu.:  3.00  
 Median : 6.000   Median :194.0   Median :  93.00   Median : 11.00  
 Mean   : 6.496   Mean   :198.9   Mean   :  94.85   Mean   : 16.81  
 3rd Qu.: 9.000   3rd Qu.:255.0   3rd Qu.: 141.00   3rd Qu.: 23.00  
 Max.   :12.000   Max.   :403.0   Max.   : 254.00   Max.   :163.00  
     nevada           prof_nieve           longitud        latitud        
 Min.   :0.000000   Min.   :   0.0000   Min.   :27.82   Min.   :-17.8889  
 1st Qu.:0.000000   1st Qu.:   0.0000   1st Qu.:38.99   1st Qu.: -4.8500  
 Median :0.000000   Median :   0.0000   Median :40.96   Median : -2.4831  
 Mean   :0.000318   Mean   :   0.4932   Mean   :40.54   Mean   : -2.4512  
 3rd Qu.:0.000000   3rd Qu.:   0.0000   3rd Qu.:42.23   3rd Qu.:  0.4942  
 Max.   :6.000000   Max.   :1834.0000   Max.   :43.57   Max.   :  4.2156  
    altitud      
 Min.   :   1.0  
 1st Qu.:  44.0  
 Median : 251.0  
 Mean   : 410.5  
 3rd Qu.: 667.0  
 Max.   :2535.0  

if (!empty_nodes) summary(df.cluster03)

   fecha_cnt           tmax            tmin            precip          nevada 
 Min.   : 1.000   Min.   : -8.0   Min.   :-58.00   Min.   :158.0   Min.   :0  
 1st Qu.: 2.000   1st Qu.:109.0   1st Qu.: 47.00   1st Qu.:171.0   1st Qu.:0  
 Median : 9.000   Median :129.0   Median : 72.00   Median :186.0   Median :0  
 Mean   : 7.207   Mean   :137.7   Mean   : 74.05   Mean   :203.4   Mean   :0  
 3rd Qu.:11.000   3rd Qu.:152.5   3rd Qu.: 92.00   3rd Qu.:220.5   3rd Qu.:0  
 Max.   :12.000   Max.   :350.0   Max.   :223.00   Max.   :422.0   Max.   :0  
   prof_nieve        longitud        latitud           altitud      
 Min.   :  0.00   Min.   :28.31   Min.   :-16.499   Min.   :   4.0  
 1st Qu.:  0.00   1st Qu.:40.78   1st Qu.: -8.624   1st Qu.:  95.0  
 Median :  0.00   Median :42.24   Median : -8.411   Median : 263.0  
 Mean   :  8.23   Mean   :40.70   Mean   : -6.597   Mean   : 566.3  
 3rd Qu.:  0.00   3rd Qu.:42.89   3rd Qu.: -4.010   3rd Qu.: 370.0  
 Max.   :607.00   Max.   :43.36   Max.   :  2.825   Max.   :2400.0  

Número de elementos en cada clúster

if (!empty_nodes) {
  df.clusters.dim <- c(dim(df.cluster01)[1], dim(df.cluster02)[1], dim(df.cluster03)[1])
  barplot(df.clusters.dim,
          names.arg = c("cluster01", "cluster02", "cluster03"),
          col = "steelblue1")
}

Distribución de los datos

if (!empty_nodes) mpr.hist(df.cluster01)

if (!empty_nodes) mpr.hist(df.cluster02)

if (!empty_nodes) mpr.hist(df.cluster03)

if (!empty_nodes) mpr.boxplot(df.cluster01)
if (!empty_nodes) mpr.boxplot(df.cluster02)

if (!empty_nodes) mpr.boxplot(df.cluster03)

Localización geográfica de las estaciones de medida de cada cluster

# Agrupa por longitud y latitud para rellenar el mapa con menos datos.
if (!empty_nodes) {
  df.cluster01.grouped <- mpr.group_by_geo(df.cluster01)
  df.cluster02.grouped <- mpr.group_by_geo(df.cluster02)
  df.cluster03.grouped <- mpr.group_by_geo(df.cluster03)
}

if (!empty_nodes) mpr.draw_map(spain, df.cluster01.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster02.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster03.grouped)

Visualización de 4 clústeres:

if (!empty_nodes) {
  plot(hac, hang=-1, labels=F)
  rect.hclust(hac, k=4)
}

Visualización de los clústers en el mapa

A qué clúster pertenece cada nodo del mapa de kohonen:

if (!empty_nodes) {
  groups <- cutree(hac, k=4)
  plot(model, type="mapping",
    bgcol=c("steelblue1","sienna1","yellowgreen","red","blue","yellow","purple","green","white","#1f77b4", '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2')[groups],
    shape = "straight", labels = "")
  add.cluster.boundaries(model, clustering=groups)
}

Análisis de las observaciones de cada cluster

if (!empty_nodes) {
  # Asignamos a cada registro su clúster
  df$cluster <- groups[model$unit.classif]
}

Nuevos dataframes por cluster

if (!empty_nodes) {
  # Creo nuevos dataframes, uno por cada clúster.
  df.cluster01 <- subset(df, cluster==1)
  df.cluster02 <- subset(df, cluster==2)
  df.cluster03 <- subset(df, cluster==3)
  df.cluster04 <- subset(df, cluster==4)

  # Extraigo del dataframe las features.
  df.cluster01 <- select(df.cluster01, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster02 <- select(df.cluster02, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster03 <- select(df.cluster03, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster04 <- select(df.cluster04, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
}

if (!empty_nodes) summary(df.cluster01)

   fecha_cnt           tmax            tmin           precip            nevada 
 Min.   : 1.000   Min.   : 13.0   Min.   :-33.0   Min.   :  0.000   Min.   :0  
 1st Qu.: 4.000   1st Qu.:201.0   1st Qu.:132.0   1st Qu.:  0.000   1st Qu.:0  
 Median : 6.000   Median :225.0   Median :160.0   Median :  1.000   Median :0  
 Mean   : 6.497   Mean   :217.5   Mean   :150.7   Mean   :  6.631   Mean   :0  
 3rd Qu.: 9.000   3rd Qu.:253.0   3rd Qu.:190.0   3rd Qu.:  8.000   3rd Qu.:0  
 Max.   :12.000   Max.   :356.0   Max.   :244.0   Max.   :130.000   Max.   :0  
   prof_nieve          longitud        latitud          altitud      
 Min.   : 0.00000   Min.   :27.82   Min.   :-17.89   Min.   :  14.0  
 1st Qu.: 0.00000   1st Qu.:28.31   1st Qu.:-16.50   1st Qu.:  25.0  
 Median : 0.00000   Median :28.44   Median :-16.33   Median :  35.0  
 Mean   : 0.03099   Mean   :28.36   Mean   :-16.05   Mean   : 519.8  
 3rd Qu.: 0.00000   3rd Qu.:28.48   3rd Qu.:-15.39   3rd Qu.: 632.0  
 Max.   :46.00000   Max.   :28.95   Max.   :-13.60   Max.   :2371.0  

if (!empty_nodes) summary(df.cluster02)

   fecha_cnt           tmax            tmin             precip      
 Min.   : 1.000   Min.   :-53.0   Min.   :-121.00   Min.   :  0.00  
 1st Qu.: 4.000   1st Qu.:148.0   1st Qu.:  49.00   1st Qu.:  3.00  
 Median : 6.000   Median :197.0   Median :  94.00   Median : 10.00  
 Mean   : 6.457   Mean   :200.9   Mean   :  95.14   Mean   : 13.54  
 3rd Qu.: 9.000   3rd Qu.:258.0   3rd Qu.: 143.00   3rd Qu.: 20.00  
 Max.   :12.000   Max.   :403.0   Max.   : 254.00   Max.   :122.00  
     nevada           prof_nieve           longitud        latitud       
 Min.   :0.000000   Min.   :   0.0000   Min.   :35.28   Min.   :-8.6494  
 1st Qu.:0.000000   1st Qu.:   0.0000   1st Qu.:38.99   1st Qu.:-4.8500  
 Median :0.000000   Median :   0.0000   Median :40.95   Median :-2.4831  
 Mean   :0.000338   Mean   :   0.4955   Mean   :40.50   Mean   :-2.3879  
 3rd Qu.:0.000000   3rd Qu.:   0.0000   3rd Qu.:42.08   3rd Qu.: 0.4942  
 Max.   :6.000000   Max.   :1834.0000   Max.   :43.57   Max.   : 4.2156  
    altitud      
 Min.   :   1.0  
 1st Qu.:  44.0  
 Median : 261.0  
 Mean   : 423.2  
 3rd Qu.: 674.0  
 Max.   :2535.0  

if (!empty_nodes) summary(df.cluster03)

   fecha_cnt           tmax            tmin             precip      
 Min.   : 1.000   Min.   :-42.0   Min.   :-102.00   Min.   : 42.00  
 1st Qu.: 3.000   1st Qu.:134.0   1st Qu.:  62.00   1st Qu.: 53.00  
 Median : 9.000   Median :162.0   Median :  86.00   Median : 64.00  
 Mean   : 7.147   Mean   :166.6   Mean   :  90.07   Mean   : 70.73  
 3rd Qu.:11.000   3rd Qu.:196.0   3rd Qu.: 117.00   3rd Qu.: 81.00  
 Max.   :12.000   Max.   :352.0   Max.   : 219.00   Max.   :163.00  
     nevada    prof_nieve          longitud        latitud       
 Min.   :0   Min.   :  0.0000   Min.   :27.82   Min.   :-17.889  
 1st Qu.:0   1st Qu.:  0.0000   1st Qu.:39.56   1st Qu.: -6.257  
 Median :0   Median :  0.0000   Median :42.24   Median : -3.788  
 Mean   :0   Mean   :  0.4554   Mean   :41.14   Mean   : -3.494  
 3rd Qu.:0   3rd Qu.:  0.0000   3rd Qu.:43.31   3rd Qu.: -1.033  
 Max.   :0   Max.   :892.0000   Max.   :43.57   Max.   :  4.216  
    altitud      
 Min.   :   1.0  
 1st Qu.:  29.0  
 Median :  91.1  
 Mean   : 201.6  
 3rd Qu.: 261.0  
 Max.   :2371.0  

if (!empty_nodes) summary(df.cluster04)

   fecha_cnt           tmax            tmin            precip          nevada 
 Min.   : 1.000   Min.   : -8.0   Min.   :-58.00   Min.   :158.0   Min.   :0  
 1st Qu.: 2.000   1st Qu.:109.0   1st Qu.: 47.00   1st Qu.:171.0   1st Qu.:0  
 Median : 9.000   Median :129.0   Median : 72.00   Median :186.0   Median :0  
 Mean   : 7.207   Mean   :137.7   Mean   : 74.05   Mean   :203.4   Mean   :0  
 3rd Qu.:11.000   3rd Qu.:152.5   3rd Qu.: 92.00   3rd Qu.:220.5   3rd Qu.:0  
 Max.   :12.000   Max.   :350.0   Max.   :223.00   Max.   :422.0   Max.   :0  
   prof_nieve        longitud        latitud           altitud      
 Min.   :  0.00   Min.   :28.31   Min.   :-16.499   Min.   :   4.0  
 1st Qu.:  0.00   1st Qu.:40.78   1st Qu.: -8.624   1st Qu.:  95.0  
 Median :  0.00   Median :42.24   Median : -8.411   Median : 263.0  
 Mean   :  8.23   Mean   :40.70   Mean   : -6.597   Mean   : 566.3  
 3rd Qu.:  0.00   3rd Qu.:42.89   3rd Qu.: -4.010   3rd Qu.: 370.0  
 Max.   :607.00   Max.   :43.36   Max.   :  2.825   Max.   :2400.0  

Número de elementos en cada clúster

if (!empty_nodes) {
  df.clusters.dim <- c(dim(df.cluster01)[1], dim(df.cluster02)[1], dim(df.cluster03)[1], dim(df.cluster04)[1])
  barplot(df.clusters.dim,
          names.arg = c("cluster01", "cluster02", "cluster03", "cluster04"),
          col = "steelblue1")
}

Distribución de los datos

if (!empty_nodes) mpr.hist(df.cluster01)

if (!empty_nodes) mpr.hist(df.cluster02)

if (!empty_nodes) mpr.hist(df.cluster03)

if (!empty_nodes) mpr.hist(df.cluster04)

if (!empty_nodes) mpr.boxplot(df.cluster01)
if (!empty_nodes) mpr.boxplot(df.cluster02)

if (!empty_nodes) mpr.boxplot(df.cluster03)

if (!empty_nodes) mpr.boxplot(df.cluster04)

Localización geográfica de las estaciones de medida de cada cluster

# Agrupa por longitud y latitud para rellenar el mapa con menos datos.
if (!empty_nodes) {
  df.cluster01.grouped <- mpr.group_by_geo(df.cluster01)
  df.cluster02.grouped <- mpr.group_by_geo(df.cluster02)
  df.cluster03.grouped <- mpr.group_by_geo(df.cluster03)
  df.cluster04.grouped <- mpr.group_by_geo(df.cluster04)
}

if (!empty_nodes) mpr.draw_map(spain, df.cluster01.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster02.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster03.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster04.grouped)

Visualización de 5 clústeres:

if (!empty_nodes) {
  plot(hac, hang=-1, labels=F)
  rect.hclust(hac, k=5)
}

Visualización de los clústers en el mapa

A qué clúster pertenece cada nodo del mapa de kohonen:

if (!empty_nodes) {
  groups <- cutree(hac, k=5)
  plot(model, type="mapping",
    bgcol=c("steelblue1","sienna1","yellowgreen","red","blue","yellow","purple","green","white","#1f77b4", '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2')[groups],
    shape = "straight", labels = "")
  add.cluster.boundaries(model, clustering=groups)
}

Análisis de las observaciones de cada cluster

if (!empty_nodes) {
  # Asignamos a cada registro su clúster
  df$cluster <- groups[model$unit.classif]
}

Nuevos dataframes por cluster

if (!empty_nodes) {
  # Creo nuevos dataframes, uno por cada clúster.
  df.cluster01 <- subset(df, cluster==1)
  df.cluster02 <- subset(df, cluster==2)
  df.cluster03 <- subset(df, cluster==3)
  df.cluster04 <- subset(df, cluster==4)
  df.cluster05 <- subset(df, cluster==5)

  # Extraigo del dataframe las features.
  df.cluster01 <- select(df.cluster01, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster02 <- select(df.cluster02, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster03 <- select(df.cluster03, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster04 <- select(df.cluster04, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster05 <- select(df.cluster05, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
}

if (!empty_nodes) summary(df.cluster01)

   fecha_cnt           tmax            tmin           precip            nevada 
 Min.   : 1.000   Min.   : 13.0   Min.   :-33.0   Min.   :  0.000   Min.   :0  
 1st Qu.: 4.000   1st Qu.:201.0   1st Qu.:132.0   1st Qu.:  0.000   1st Qu.:0  
 Median : 6.000   Median :225.0   Median :160.0   Median :  1.000   Median :0  
 Mean   : 6.497   Mean   :217.5   Mean   :150.7   Mean   :  6.631   Mean   :0  
 3rd Qu.: 9.000   3rd Qu.:253.0   3rd Qu.:190.0   3rd Qu.:  8.000   3rd Qu.:0  
 Max.   :12.000   Max.   :356.0   Max.   :244.0   Max.   :130.000   Max.   :0  
   prof_nieve          longitud        latitud          altitud      
 Min.   : 0.00000   Min.   :27.82   Min.   :-17.89   Min.   :  14.0  
 1st Qu.: 0.00000   1st Qu.:28.31   1st Qu.:-16.50   1st Qu.:  25.0  
 Median : 0.00000   Median :28.44   Median :-16.33   Median :  35.0  
 Mean   : 0.03099   Mean   :28.36   Mean   :-16.05   Mean   : 519.8  
 3rd Qu.: 0.00000   3rd Qu.:28.48   3rd Qu.:-15.39   3rd Qu.: 632.0  
 Max.   :46.00000   Max.   :28.95   Max.   :-13.60   Max.   :2371.0  

if (!empty_nodes) summary(df.cluster02)

   fecha_cnt           tmax            tmin             precip     
 Min.   : 1.000   Min.   : -4.0   Min.   :-110.00   Min.   : 0.00  
 1st Qu.: 4.000   1st Qu.:152.0   1st Qu.:  52.00   1st Qu.: 3.00  
 Median : 6.000   Median :200.0   Median :  96.00   Median :10.00  
 Mean   : 6.456   Mean   :204.7   Mean   :  97.84   Mean   :12.87  
 3rd Qu.: 9.000   3rd Qu.:261.0   3rd Qu.: 145.00   3rd Qu.:20.00  
 Max.   :12.000   Max.   :403.0   Max.   : 254.00   Max.   :65.00  
     nevada           prof_nieve           longitud        latitud       
 Min.   :0.000000   Min.   :  0.00000   Min.   :35.28   Min.   :-8.6494  
 1st Qu.:0.000000   1st Qu.:  0.00000   1st Qu.:38.95   1st Qu.:-4.8500  
 Median :0.000000   Median :  0.00000   Median :40.95   Median :-2.6544  
 Mean   :0.000349   Mean   :  0.06075   Mean   :40.46   Mean   :-2.4598  
 3rd Qu.:0.000000   3rd Qu.:  0.00000   3rd Qu.:42.01   3rd Qu.: 0.4914  
 Max.   :6.000000   Max.   :229.00000   Max.   :43.57   Max.   : 4.2156  
    altitud      
 Min.   :   1.0  
 1st Qu.:  44.0  
 Median : 251.0  
 Mean   : 367.6  
 3rd Qu.: 656.0  
 Max.   :1894.0  

if (!empty_nodes) summary(df.cluster03)

   fecha_cnt           tmax            tmin             precip      
 Min.   : 1.000   Min.   :-42.0   Min.   :-102.00   Min.   : 42.00  
 1st Qu.: 3.000   1st Qu.:134.0   1st Qu.:  62.00   1st Qu.: 53.00  
 Median : 9.000   Median :162.0   Median :  86.00   Median : 64.00  
 Mean   : 7.147   Mean   :166.6   Mean   :  90.07   Mean   : 70.73  
 3rd Qu.:11.000   3rd Qu.:196.0   3rd Qu.: 117.00   3rd Qu.: 81.00  
 Max.   :12.000   Max.   :352.0   Max.   : 219.00   Max.   :163.00  
     nevada    prof_nieve          longitud        latitud       
 Min.   :0   Min.   :  0.0000   Min.   :27.82   Min.   :-17.889  
 1st Qu.:0   1st Qu.:  0.0000   1st Qu.:39.56   1st Qu.: -6.257  
 Median :0   Median :  0.0000   Median :42.24   Median : -3.788  
 Mean   :0   Mean   :  0.4554   Mean   :41.14   Mean   : -3.494  
 3rd Qu.:0   3rd Qu.:  0.0000   3rd Qu.:43.31   3rd Qu.: -1.033  
 Max.   :0   Max.   :892.0000   Max.   :43.57   Max.   :  4.216  
    altitud      
 Min.   :   1.0  
 1st Qu.:  29.0  
 Median :  91.1  
 Mean   : 201.6  
 3rd Qu.: 261.0  
 Max.   :2371.0  

if (!empty_nodes) summary(df.cluster04)

   fecha_cnt           tmax            tmin            precip          nevada 
 Min.   : 1.000   Min.   : -8.0   Min.   :-58.00   Min.   :158.0   Min.   :0  
 1st Qu.: 2.000   1st Qu.:109.0   1st Qu.: 47.00   1st Qu.:171.0   1st Qu.:0  
 Median : 9.000   Median :129.0   Median : 72.00   Median :186.0   Median :0  
 Mean   : 7.207   Mean   :137.7   Mean   : 74.05   Mean   :203.4   Mean   :0  
 3rd Qu.:11.000   3rd Qu.:152.5   3rd Qu.: 92.00   3rd Qu.:220.5   3rd Qu.:0  
 Max.   :12.000   Max.   :350.0   Max.   :223.00   Max.   :422.0   Max.   :0  
   prof_nieve        longitud        latitud           altitud      
 Min.   :  0.00   Min.   :28.31   Min.   :-16.499   Min.   :   4.0  
 1st Qu.:  0.00   1st Qu.:40.78   1st Qu.: -8.624   1st Qu.:  95.0  
 Median :  0.00   Median :42.24   Median : -8.411   Median : 263.0  
 Mean   :  8.23   Mean   :40.70   Mean   : -6.597   Mean   : 566.3  
 3rd Qu.:  0.00   3rd Qu.:42.89   3rd Qu.: -4.010   3rd Qu.: 370.0  
 Max.   :607.00   Max.   :43.36   Max.   :  2.825   Max.   :2400.0  

if (!empty_nodes) summary(df.cluster05)

   fecha_cnt           tmax             tmin             precip      
 Min.   : 1.000   Min.   :-53.00   Min.   :-121.00   Min.   :  0.00  
 1st Qu.: 4.000   1st Qu.: 29.00   1st Qu.: -33.00   1st Qu.: 19.00  
 Median : 6.000   Median : 72.00   Median :   6.00   Median : 30.00  
 Mean   : 6.482   Mean   : 80.96   Mean   :  11.65   Mean   : 34.35  
 3rd Qu.:10.000   3rd Qu.:131.00   3rd Qu.:  56.25   3rd Qu.: 47.00  
 Max.   :12.000   Max.   :263.00   Max.   : 163.00   Max.   :122.00  
     nevada    prof_nieve         longitud        latitud           altitud    
 Min.   :0   Min.   :   0.00   Min.   :40.78   Min.   :-4.0103   Min.   :1572  
 1st Qu.:0   1st Qu.:   0.00   1st Qu.:40.78   1st Qu.:-4.0103   1st Qu.:1894  
 Median :0   Median :   0.00   Median :42.38   Median : 0.9844   Median :2228  
 Mean   :0   Mean   :  13.97   Mean   :41.99   Mean   :-0.1576   Mean   :2145  
 3rd Qu.:0   3rd Qu.:   0.00   3rd Qu.:42.53   3rd Qu.: 1.5242   3rd Qu.:2316  
 Max.   :0   Max.   :1834.00   Max.   :42.77   Max.   : 2.4378   Max.   :2535  

Número de elementos en cada clúster

if (!empty_nodes) {
  df.clusters.dim <- c(dim(df.cluster01)[1], dim(df.cluster02)[1], dim(df.cluster03)[1], dim(df.cluster04)[1], dim(df.cluster05)[1])
  barplot(df.clusters.dim,
          names.arg = c("cluster01", "cluster02", "cluster03", "cluster04", "cluster05"),
          col = "steelblue1")
}

Distribución de los datos

if (!empty_nodes) mpr.hist(df.cluster01)

if (!empty_nodes) mpr.hist(df.cluster02)

if (!empty_nodes) mpr.hist(df.cluster03)

if (!empty_nodes) mpr.hist(df.cluster04)

if (!empty_nodes) mpr.hist(df.cluster05)

if (!empty_nodes) mpr.boxplot(df.cluster01)
if (!empty_nodes) mpr.boxplot(df.cluster02)

if (!empty_nodes) mpr.boxplot(df.cluster03)

if (!empty_nodes) mpr.boxplot(df.cluster04)

if (!empty_nodes) mpr.boxplot(df.cluster05)

Localización geográfica de las estaciones de medida de cada cluster

# Agrupa por longitud y latitud para rellenar el mapa con menos datos.
if (!empty_nodes) {
  df.cluster01.grouped <- mpr.group_by_geo(df.cluster01)
  df.cluster02.grouped <- mpr.group_by_geo(df.cluster02)
  df.cluster03.grouped <- mpr.group_by_geo(df.cluster03)
  df.cluster04.grouped <- mpr.group_by_geo(df.cluster04)
  df.cluster05.grouped <- mpr.group_by_geo(df.cluster05)
}

if (!empty_nodes) mpr.draw_map(spain, df.cluster01.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster02.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster03.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster04.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster05.grouped)

Visualización de 6 clústeres:

if (!empty_nodes) {
  plot(hac, hang=-1, labels=F)
  rect.hclust(hac, k=6)
}

Visualización de los clústers en el mapa

A qué clúster pertenece cada nodo del mapa de kohonen:

if (!empty_nodes) {
  groups <- cutree(hac, k=6)
  plot(model, type="mapping",
    bgcol=c("steelblue1","sienna1","yellowgreen","red","blue","yellow","purple","green","white","#1f77b4", '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2')[groups],
    shape = "straight", labels = "")
  add.cluster.boundaries(model, clustering=groups)
}

Análisis de las observaciones de cada cluster

if (!empty_nodes) {
  # Asignamos a cada registro su clúster
  df$cluster <- groups[model$unit.classif]
}

Nuevos dataframes por cluster

if (!empty_nodes) {
  # Creo nuevos dataframes, uno por cada clúster.
  df.cluster01 <- subset(df, cluster==1)
  df.cluster02 <- subset(df, cluster==2)
  df.cluster03 <- subset(df, cluster==3)
  df.cluster04 <- subset(df, cluster==4)
  df.cluster05 <- subset(df, cluster==5)
  df.cluster06 <- subset(df, cluster==6)

  # Extraigo del dataframe las features.
  df.cluster01 <- select(df.cluster01, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster02 <- select(df.cluster02, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster03 <- select(df.cluster03, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster04 <- select(df.cluster04, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster05 <- select(df.cluster05, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster06 <- select(df.cluster06, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
}

if (!empty_nodes) summary(df.cluster01)

   fecha_cnt           tmax            tmin           precip           nevada 
 Min.   : 1.000   Min.   : 87.0   Min.   : 64.0   Min.   : 0.000   Min.   :0  
 1st Qu.: 3.000   1st Qu.:212.0   1st Qu.:150.0   1st Qu.: 0.000   1st Qu.:0  
 Median : 6.000   Median :233.0   Median :169.0   Median : 1.000   Median :0  
 Mean   : 6.478   Mean   :234.1   Mean   :169.6   Mean   : 5.838   Mean   :0  
 3rd Qu.: 9.000   3rd Qu.:259.0   3rd Qu.:196.0   3rd Qu.: 7.000   3rd Qu.:0  
 Max.   :12.000   Max.   :356.0   Max.   :244.0   Max.   :73.000   Max.   :0  
   prof_nieve          longitud        latitud          altitud     
 Min.   :0.000000   Min.   :27.82   Min.   :-17.89   Min.   : 14.0  
 1st Qu.:0.000000   1st Qu.:28.05   1st Qu.:-16.56   1st Qu.: 25.0  
 Median :0.000000   Median :28.46   Median :-16.26   Median : 33.0  
 Mean   :0.000354   Mean   :28.38   Mean   :-15.95   Mean   :129.9  
 3rd Qu.:0.000000   3rd Qu.:28.48   3rd Qu.:-15.39   3rd Qu.: 64.0  
 Max.   :2.000000   Max.   :28.95   Max.   :-13.60   Max.   :632.0  

if (!empty_nodes) summary(df.cluster02)

   fecha_cnt           tmax            tmin             precip     
 Min.   : 1.000   Min.   : -4.0   Min.   :-110.00   Min.   : 0.00  
 1st Qu.: 4.000   1st Qu.:152.0   1st Qu.:  52.00   1st Qu.: 3.00  
 Median : 6.000   Median :200.0   Median :  96.00   Median :10.00  
 Mean   : 6.456   Mean   :204.7   Mean   :  97.84   Mean   :12.87  
 3rd Qu.: 9.000   3rd Qu.:261.0   3rd Qu.: 145.00   3rd Qu.:20.00  
 Max.   :12.000   Max.   :403.0   Max.   : 254.00   Max.   :65.00  
     nevada           prof_nieve           longitud        latitud       
 Min.   :0.000000   Min.   :  0.00000   Min.   :35.28   Min.   :-8.6494  
 1st Qu.:0.000000   1st Qu.:  0.00000   1st Qu.:38.95   1st Qu.:-4.8500  
 Median :0.000000   Median :  0.00000   Median :40.95   Median :-2.6544  
 Mean   :0.000349   Mean   :  0.06075   Mean   :40.46   Mean   :-2.4598  
 3rd Qu.:0.000000   3rd Qu.:  0.00000   3rd Qu.:42.01   3rd Qu.: 0.4914  
 Max.   :6.000000   Max.   :229.00000   Max.   :43.57   Max.   : 4.2156  
    altitud      
 Min.   :   1.0  
 1st Qu.:  44.0  
 Median : 251.0  
 Mean   : 367.6  
 3rd Qu.: 656.0  
 Max.   :1894.0  

if (!empty_nodes) summary(df.cluster03)

   fecha_cnt           tmax            tmin             precip      
 Min.   : 1.000   Min.   :-42.0   Min.   :-102.00   Min.   : 42.00  
 1st Qu.: 3.000   1st Qu.:134.0   1st Qu.:  62.00   1st Qu.: 53.00  
 Median : 9.000   Median :162.0   Median :  86.00   Median : 64.00  
 Mean   : 7.147   Mean   :166.6   Mean   :  90.07   Mean   : 70.73  
 3rd Qu.:11.000   3rd Qu.:196.0   3rd Qu.: 117.00   3rd Qu.: 81.00  
 Max.   :12.000   Max.   :352.0   Max.   : 219.00   Max.   :163.00  
     nevada    prof_nieve          longitud        latitud       
 Min.   :0   Min.   :  0.0000   Min.   :27.82   Min.   :-17.889  
 1st Qu.:0   1st Qu.:  0.0000   1st Qu.:39.56   1st Qu.: -6.257  
 Median :0   Median :  0.0000   Median :42.24   Median : -3.788  
 Mean   :0   Mean   :  0.4554   Mean   :41.14   Mean   : -3.494  
 3rd Qu.:0   3rd Qu.:  0.0000   3rd Qu.:43.31   3rd Qu.: -1.033  
 Max.   :0   Max.   :892.0000   Max.   :43.57   Max.   :  4.216  
    altitud      
 Min.   :   1.0  
 1st Qu.:  29.0  
 Median :  91.1  
 Mean   : 201.6  
 3rd Qu.: 261.0  
 Max.   :2371.0  

if (!empty_nodes) summary(df.cluster04)

   fecha_cnt           tmax            tmin            precip           nevada 
 Min.   : 1.000   Min.   : 13.0   Min.   :-33.00   Min.   :  0.00   Min.   :0  
 1st Qu.: 4.000   1st Qu.: 94.0   1st Qu.: 23.00   1st Qu.:  0.00   1st Qu.:0  
 Median : 7.000   Median :129.0   Median : 50.50   Median :  3.00   Median :0  
 Mean   : 6.588   Mean   :138.4   Mean   : 60.82   Mean   : 10.39   Mean   :0  
 3rd Qu.:10.000   3rd Qu.:183.0   3rd Qu.: 98.00   3rd Qu.: 13.00   3rd Qu.:0  
 Max.   :12.000   Max.   :253.0   Max.   :159.00   Max.   :130.00   Max.   :0  
   prof_nieve         longitud        latitud         altitud    
 Min.   : 0.0000   Min.   :28.31   Min.   :-16.5   Min.   :2371  
 1st Qu.: 0.0000   1st Qu.:28.31   1st Qu.:-16.5   1st Qu.:2371  
 Median : 0.0000   Median :28.31   Median :-16.5   Median :2371  
 Mean   : 0.1765   Mean   :28.31   Mean   :-16.5   Mean   :2371  
 3rd Qu.: 0.0000   3rd Qu.:28.31   3rd Qu.:-16.5   3rd Qu.:2371  
 Max.   :46.0000   Max.   :28.31   Max.   :-16.5   Max.   :2371  

if (!empty_nodes) summary(df.cluster05)

   fecha_cnt           tmax            tmin            precip          nevada 
 Min.   : 1.000   Min.   : -8.0   Min.   :-58.00   Min.   :158.0   Min.   :0  
 1st Qu.: 2.000   1st Qu.:109.0   1st Qu.: 47.00   1st Qu.:171.0   1st Qu.:0  
 Median : 9.000   Median :129.0   Median : 72.00   Median :186.0   Median :0  
 Mean   : 7.207   Mean   :137.7   Mean   : 74.05   Mean   :203.4   Mean   :0  
 3rd Qu.:11.000   3rd Qu.:152.5   3rd Qu.: 92.00   3rd Qu.:220.5   3rd Qu.:0  
 Max.   :12.000   Max.   :350.0   Max.   :223.00   Max.   :422.0   Max.   :0  
   prof_nieve        longitud        latitud           altitud      
 Min.   :  0.00   Min.   :28.31   Min.   :-16.499   Min.   :   4.0  
 1st Qu.:  0.00   1st Qu.:40.78   1st Qu.: -8.624   1st Qu.:  95.0  
 Median :  0.00   Median :42.24   Median : -8.411   Median : 263.0  
 Mean   :  8.23   Mean   :40.70   Mean   : -6.597   Mean   : 566.3  
 3rd Qu.:  0.00   3rd Qu.:42.89   3rd Qu.: -4.010   3rd Qu.: 370.0  
 Max.   :607.00   Max.   :43.36   Max.   :  2.825   Max.   :2400.0  

if (!empty_nodes) summary(df.cluster06)

   fecha_cnt           tmax             tmin             precip      
 Min.   : 1.000   Min.   :-53.00   Min.   :-121.00   Min.   :  0.00  
 1st Qu.: 4.000   1st Qu.: 29.00   1st Qu.: -33.00   1st Qu.: 19.00  
 Median : 6.000   Median : 72.00   Median :   6.00   Median : 30.00  
 Mean   : 6.482   Mean   : 80.96   Mean   :  11.65   Mean   : 34.35  
 3rd Qu.:10.000   3rd Qu.:131.00   3rd Qu.:  56.25   3rd Qu.: 47.00  
 Max.   :12.000   Max.   :263.00   Max.   : 163.00   Max.   :122.00  
     nevada    prof_nieve         longitud        latitud           altitud    
 Min.   :0   Min.   :   0.00   Min.   :40.78   Min.   :-4.0103   Min.   :1572  
 1st Qu.:0   1st Qu.:   0.00   1st Qu.:40.78   1st Qu.:-4.0103   1st Qu.:1894  
 Median :0   Median :   0.00   Median :42.38   Median : 0.9844   Median :2228  
 Mean   :0   Mean   :  13.97   Mean   :41.99   Mean   :-0.1576   Mean   :2145  
 3rd Qu.:0   3rd Qu.:   0.00   3rd Qu.:42.53   3rd Qu.: 1.5242   3rd Qu.:2316  
 Max.   :0   Max.   :1834.00   Max.   :42.77   Max.   : 2.4378   Max.   :2535  

Número de elementos en cada clúster

if (!empty_nodes) {
  df.clusters.dim <- c(dim(df.cluster01)[1], dim(df.cluster02)[1], dim(df.cluster03)[1], dim(df.cluster04)[1], dim(df.cluster05)[1], dim(df.cluster06)[1])
  barplot(df.clusters.dim,
          names.arg = c("cluster01", "cluster02", "cluster03", "cluster04", "cluster05", "cluster06"),
          col = "steelblue1")
}

Distribución de los datos

if (!empty_nodes) mpr.hist(df.cluster01)

if (!empty_nodes) mpr.hist(df.cluster02)

if (!empty_nodes) mpr.hist(df.cluster03)

if (!empty_nodes) mpr.hist(df.cluster04)

if (!empty_nodes) mpr.hist(df.cluster05)

if (!empty_nodes) mpr.hist(df.cluster06)

if (!empty_nodes) mpr.boxplot(df.cluster01)
if (!empty_nodes) mpr.boxplot(df.cluster02)

if (!empty_nodes) mpr.boxplot(df.cluster03)

if (!empty_nodes) mpr.boxplot(df.cluster04)

if (!empty_nodes) mpr.boxplot(df.cluster05)

if (!empty_nodes) mpr.boxplot(df.cluster06)

Localización geográfica de las estaciones de medida de cada cluster

# Agrupa por longitud y latitud para rellenar el mapa con menos datos.
if (!empty_nodes) {
  df.cluster01.grouped <- mpr.group_by_geo(df.cluster01)
  df.cluster02.grouped <- mpr.group_by_geo(df.cluster02)
  df.cluster03.grouped <- mpr.group_by_geo(df.cluster03)
  df.cluster04.grouped <- mpr.group_by_geo(df.cluster04)
  df.cluster05.grouped <- mpr.group_by_geo(df.cluster05)
  df.cluster06.grouped <- mpr.group_by_geo(df.cluster06)
}

if (!empty_nodes) mpr.draw_map(spain, df.cluster01.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster02.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster03.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster04.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster05.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster06.grouped)

Visualización de 8 clústeres:

if (!empty_nodes) {
  plot(hac, hang=-1, labels=F)
  rect.hclust(hac, k=8)
}

Visualización de los clústers en el mapa

A qué clúster pertenece cada nodo del mapa de kohonen:

if (!empty_nodes) {
  groups <- cutree(hac, k=8)
  plot(model, type="mapping",
    bgcol=c("steelblue1","sienna1","yellowgreen","red","blue","yellow","purple","green","white","#1f77b4", '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2')[groups],
    shape = "straight", labels = "")
  add.cluster.boundaries(model, clustering=groups)
}

Análisis de las observaciones de cada cluster

if (!empty_nodes) {
  # Asignamos a cada registro su clúster
  df$cluster <- groups[model$unit.classif]
}

Nuevos dataframes por cluster

if (!empty_nodes) {
  # Creo nuevos dataframes, uno por cada clúster.
  df.cluster01 <- subset(df, cluster==1)
  df.cluster02 <- subset(df, cluster==2)
  df.cluster03 <- subset(df, cluster==3)
  df.cluster04 <- subset(df, cluster==4)
  df.cluster05 <- subset(df, cluster==5)
  df.cluster06 <- subset(df, cluster==6)
  df.cluster07 <- subset(df, cluster==7)
  df.cluster08 <- subset(df, cluster==8)

  # Extraigo del dataframe las features.
  df.cluster01 <- select(df.cluster01, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster02 <- select(df.cluster02, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster03 <- select(df.cluster03, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster04 <- select(df.cluster04, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster05 <- select(df.cluster05, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster06 <- select(df.cluster06, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster07 <- select(df.cluster07, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster08 <- select(df.cluster08, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
}

if (!empty_nodes) summary(df.cluster01)

   fecha_cnt           tmax            tmin           precip           nevada 
 Min.   : 1.000   Min.   : 87.0   Min.   : 64.0   Min.   : 0.000   Min.   :0  
 1st Qu.: 3.000   1st Qu.:212.0   1st Qu.:150.0   1st Qu.: 0.000   1st Qu.:0  
 Median : 6.000   Median :233.0   Median :169.0   Median : 1.000   Median :0  
 Mean   : 6.478   Mean   :234.1   Mean   :169.6   Mean   : 5.838   Mean   :0  
 3rd Qu.: 9.000   3rd Qu.:259.0   3rd Qu.:196.0   3rd Qu.: 7.000   3rd Qu.:0  
 Max.   :12.000   Max.   :356.0   Max.   :244.0   Max.   :73.000   Max.   :0  
   prof_nieve          longitud        latitud          altitud     
 Min.   :0.000000   Min.   :27.82   Min.   :-17.89   Min.   : 14.0  
 1st Qu.:0.000000   1st Qu.:28.05   1st Qu.:-16.56   1st Qu.: 25.0  
 Median :0.000000   Median :28.46   Median :-16.26   Median : 33.0  
 Mean   :0.000354   Mean   :28.38   Mean   :-15.95   Mean   :129.9  
 3rd Qu.:0.000000   3rd Qu.:28.48   3rd Qu.:-15.39   3rd Qu.: 64.0  
 Max.   :2.000000   Max.   :28.95   Max.   :-13.60   Max.   :632.0  

if (!empty_nodes) summary(df.cluster02)

   fecha_cnt           tmax            tmin           precip     
 Min.   : 1.000   Min.   :  8.0   Min.   :-68.0   Min.   : 0.00  
 1st Qu.: 4.000   1st Qu.:169.0   1st Qu.: 70.0   1st Qu.: 2.00  
 Median : 6.000   Median :215.0   Median :115.0   Median : 7.00  
 Mean   : 6.446   Mean   :219.3   Mean   :115.8   Mean   :10.07  
 3rd Qu.: 9.000   3rd Qu.:272.0   3rd Qu.:165.0   3rd Qu.:15.00  
 Max.   :12.000   Max.   :403.0   Max.   :254.0   Max.   :48.00  
     nevada           prof_nieve         longitud        latitud       
 Min.   :0.000000   Min.   : 0.0000   Min.   :35.28   Min.   :-6.9492  
 1st Qu.:0.000000   1st Qu.: 0.0000   1st Qu.:37.79   1st Qu.:-2.9553  
 Median :0.000000   Median : 0.0000   Median :39.85   Median : 0.3664  
 Mean   :0.000286   Mean   : 0.0214   Mean   :39.66   Mean   :-1.0267  
 3rd Qu.:0.000000   3rd Qu.: 0.0000   3rd Qu.:41.63   3rd Qu.: 1.1789  
 Max.   :4.000000   Max.   :59.0000   Max.   :43.49   Max.   : 4.2156  
    altitud     
 Min.   :  1.0  
 1st Qu.: 21.0  
 Median : 69.0  
 Mean   :140.3  
 3rd Qu.:192.0  
 Max.   :953.0  

if (!empty_nodes) summary(df.cluster03)

   fecha_cnt           tmax            tmin             precip      
 Min.   : 1.000   Min.   :-42.0   Min.   :-102.00   Min.   : 42.00  
 1st Qu.: 3.000   1st Qu.:134.0   1st Qu.:  62.00   1st Qu.: 53.00  
 Median : 9.000   Median :162.0   Median :  86.00   Median : 64.00  
 Mean   : 7.147   Mean   :166.6   Mean   :  90.07   Mean   : 70.73  
 3rd Qu.:11.000   3rd Qu.:196.0   3rd Qu.: 117.00   3rd Qu.: 81.00  
 Max.   :12.000   Max.   :352.0   Max.   : 219.00   Max.   :163.00  
     nevada    prof_nieve          longitud        latitud       
 Min.   :0   Min.   :  0.0000   Min.   :27.82   Min.   :-17.889  
 1st Qu.:0   1st Qu.:  0.0000   1st Qu.:39.56   1st Qu.: -6.257  
 Median :0   Median :  0.0000   Median :42.24   Median : -3.788  
 Mean   :0   Mean   :  0.4554   Mean   :41.14   Mean   : -3.494  
 3rd Qu.:0   3rd Qu.:  0.0000   3rd Qu.:43.31   3rd Qu.: -1.033  
 Max.   :0   Max.   :892.0000   Max.   :43.57   Max.   :  4.216  
    altitud      
 Min.   :   1.0  
 1st Qu.:  29.0  
 Median :  91.1  
 Mean   : 201.6  
 3rd Qu.: 261.0  
 Max.   :2371.0  

if (!empty_nodes) summary(df.cluster04)

   fecha_cnt           tmax            tmin            precip           nevada 
 Min.   : 1.000   Min.   : 13.0   Min.   :-33.00   Min.   :  0.00   Min.   :0  
 1st Qu.: 4.000   1st Qu.: 94.0   1st Qu.: 23.00   1st Qu.:  0.00   1st Qu.:0  
 Median : 7.000   Median :129.0   Median : 50.50   Median :  3.00   Median :0  
 Mean   : 6.588   Mean   :138.4   Mean   : 60.82   Mean   : 10.39   Mean   :0  
 3rd Qu.:10.000   3rd Qu.:183.0   3rd Qu.: 98.00   3rd Qu.: 13.00   3rd Qu.:0  
 Max.   :12.000   Max.   :253.0   Max.   :159.00   Max.   :130.00   Max.   :0  
   prof_nieve         longitud        latitud         altitud    
 Min.   : 0.0000   Min.   :28.31   Min.   :-16.5   Min.   :2371  
 1st Qu.: 0.0000   1st Qu.:28.31   1st Qu.:-16.5   1st Qu.:2371  
 Median : 0.0000   Median :28.31   Median :-16.5   Median :2371  
 Mean   : 0.1765   Mean   :28.31   Mean   :-16.5   Mean   :2371  
 3rd Qu.: 0.0000   3rd Qu.:28.31   3rd Qu.:-16.5   3rd Qu.:2371  
 Max.   :46.0000   Max.   :28.31   Max.   :-16.5   Max.   :2371  

if (!empty_nodes) summary(df.cluster05)

   fecha_cnt           tmax            tmin             precip     
 Min.   : 1.000   Min.   : -4.0   Min.   :-110.00   Min.   : 0.00  
 1st Qu.: 4.000   1st Qu.:123.0   1st Qu.:  23.00   1st Qu.: 3.00  
 Median : 6.000   Median :183.0   Median :  66.00   Median : 9.00  
 Mean   : 6.484   Mean   :191.8   Mean   :  71.34   Mean   :12.15  
 3rd Qu.: 9.000   3rd Qu.:261.0   3rd Qu.: 117.00   3rd Qu.:18.00  
 Max.   :12.000   Max.   :400.0   Max.   : 250.00   Max.   :65.00  
     nevada           prof_nieve          longitud        latitud      
 Min.   :0.000000   Min.   :  0.0000   Min.   :37.13   Min.   :-6.600  
 1st Qu.:0.000000   1st Qu.:  0.0000   1st Qu.:40.07   1st Qu.:-4.680  
 Median :0.000000   Median :  0.0000   Median :40.70   Median :-3.764  
 Mean   :0.000498   Mean   :  0.1203   Mean   :40.67   Mean   :-3.352  
 3rd Qu.:0.000000   3rd Qu.:  0.0000   3rd Qu.:41.65   3rd Qu.:-2.138  
 Max.   :6.000000   Max.   :229.0000   Max.   :42.70   Max.   : 2.438  
    altitud      
 Min.   : 405.0  
 1st Qu.: 627.0  
 Median : 735.0  
 Mean   : 777.2  
 3rd Qu.: 916.0  
 Max.   :1894.0  

if (!empty_nodes) summary(df.cluster06)

   fecha_cnt           tmax            tmin            precip          nevada 
 Min.   : 1.000   Min.   : -8.0   Min.   :-58.00   Min.   :158.0   Min.   :0  
 1st Qu.: 2.000   1st Qu.:109.0   1st Qu.: 47.00   1st Qu.:171.0   1st Qu.:0  
 Median : 9.000   Median :129.0   Median : 72.00   Median :186.0   Median :0  
 Mean   : 7.207   Mean   :137.7   Mean   : 74.05   Mean   :203.4   Mean   :0  
 3rd Qu.:11.000   3rd Qu.:152.5   3rd Qu.: 92.00   3rd Qu.:220.5   3rd Qu.:0  
 Max.   :12.000   Max.   :350.0   Max.   :223.00   Max.   :422.0   Max.   :0  
   prof_nieve        longitud        latitud           altitud      
 Min.   :  0.00   Min.   :28.31   Min.   :-16.499   Min.   :   4.0  
 1st Qu.:  0.00   1st Qu.:40.78   1st Qu.: -8.624   1st Qu.:  95.0  
 Median :  0.00   Median :42.24   Median : -8.411   Median : 263.0  
 Mean   :  8.23   Mean   :40.70   Mean   : -6.597   Mean   : 566.3  
 3rd Qu.:  0.00   3rd Qu.:42.89   3rd Qu.: -4.010   3rd Qu.: 370.0  
 Max.   :607.00   Max.   :43.36   Max.   :  2.825   Max.   :2400.0  

if (!empty_nodes) summary(df.cluster07)

   fecha_cnt           tmax            tmin            precip     
 Min.   : 1.000   Min.   :  7.0   Min.   :-62.00   Min.   : 0.00  
 1st Qu.: 4.000   1st Qu.:143.0   1st Qu.: 65.00   1st Qu.:16.00  
 Median : 6.000   Median :179.0   Median : 96.00   Median :26.00  
 Mean   : 6.419   Mean   :180.8   Mean   : 96.97   Mean   :26.18  
 3rd Qu.: 9.000   3rd Qu.:219.0   3rd Qu.:133.00   3rd Qu.:36.00  
 Max.   :12.000   Max.   :340.0   Max.   :202.00   Max.   :59.00  
     nevada            prof_nieve          longitud        latitud      
 Min.   :0.0000000   Min.   : 0.00000   Min.   :39.47   Min.   :-8.649  
 1st Qu.:0.0000000   1st Qu.: 0.00000   1st Qu.:42.85   1st Qu.:-8.411  
 Median :0.0000000   Median : 0.00000   Median :43.31   Median :-5.873  
 Mean   :0.0001935   Mean   : 0.05853   Mean   :43.11   Mean   :-5.845  
 3rd Qu.:0.0000000   3rd Qu.: 0.00000   3rd Qu.:43.43   3rd Qu.:-3.819  
 Max.   :2.0000000   Max.   :40.00000   Max.   :43.57   Max.   :-1.008  
    altitud     
 Min.   :  4.0  
 1st Qu.: 42.0  
 Median :108.0  
 Mean   :176.5  
 3rd Qu.:336.0  
 Max.   :656.0  

if (!empty_nodes) summary(df.cluster08)

   fecha_cnt           tmax             tmin             precip      
 Min.   : 1.000   Min.   :-53.00   Min.   :-121.00   Min.   :  0.00  
 1st Qu.: 4.000   1st Qu.: 29.00   1st Qu.: -33.00   1st Qu.: 19.00  
 Median : 6.000   Median : 72.00   Median :   6.00   Median : 30.00  
 Mean   : 6.482   Mean   : 80.96   Mean   :  11.65   Mean   : 34.35  
 3rd Qu.:10.000   3rd Qu.:131.00   3rd Qu.:  56.25   3rd Qu.: 47.00  
 Max.   :12.000   Max.   :263.00   Max.   : 163.00   Max.   :122.00  
     nevada    prof_nieve         longitud        latitud           altitud    
 Min.   :0   Min.   :   0.00   Min.   :40.78   Min.   :-4.0103   Min.   :1572  
 1st Qu.:0   1st Qu.:   0.00   1st Qu.:40.78   1st Qu.:-4.0103   1st Qu.:1894  
 Median :0   Median :   0.00   Median :42.38   Median : 0.9844   Median :2228  
 Mean   :0   Mean   :  13.97   Mean   :41.99   Mean   :-0.1576   Mean   :2145  
 3rd Qu.:0   3rd Qu.:   0.00   3rd Qu.:42.53   3rd Qu.: 1.5242   3rd Qu.:2316  
 Max.   :0   Max.   :1834.00   Max.   :42.77   Max.   : 2.4378   Max.   :2535  

Número de elementos en cada clúster

if (!empty_nodes) {
  df.clusters.dim <- c(dim(df.cluster01)[1], dim(df.cluster02)[1], dim(df.cluster03)[1], dim(df.cluster04)[1], dim(df.cluster05)[1], dim(df.cluster06)[1], dim(df.cluster07)[1], dim(df.cluster08)[1])
  barplot(df.clusters.dim,
          names.arg = c("cluster01", "cluster02", "cluster03", "cluster04", "cluster05", "cluster06", "cluster07", "cluster08"),
          col = "steelblue1")
}

Distribución de los datos

if (!empty_nodes) mpr.hist(df.cluster01)

if (!empty_nodes) mpr.hist(df.cluster02)

if (!empty_nodes) mpr.hist(df.cluster03)

if (!empty_nodes) mpr.hist(df.cluster04)

if (!empty_nodes) mpr.hist(df.cluster05)

if (!empty_nodes) mpr.hist(df.cluster06)

if (!empty_nodes) mpr.hist(df.cluster07)

if (!empty_nodes) mpr.hist(df.cluster08)

if (!empty_nodes) mpr.boxplot(df.cluster01)
if (!empty_nodes) mpr.boxplot(df.cluster02)

if (!empty_nodes) mpr.boxplot(df.cluster03)

if (!empty_nodes) mpr.boxplot(df.cluster04)

if (!empty_nodes) mpr.boxplot(df.cluster05)

if (!empty_nodes) mpr.boxplot(df.cluster06)

if (!empty_nodes) mpr.boxplot(df.cluster07)

if (!empty_nodes) mpr.boxplot(df.cluster08)

Localización geográfica de las estaciones de medida de cada cluster

# Agrupa por longitud y latitud para rellenar el mapa con menos datos.
if (!empty_nodes) {
  df.cluster01.grouped <- mpr.group_by_geo(df.cluster01)
  df.cluster02.grouped <- mpr.group_by_geo(df.cluster02)
  df.cluster03.grouped <- mpr.group_by_geo(df.cluster03)
  df.cluster04.grouped <- mpr.group_by_geo(df.cluster04)
  df.cluster05.grouped <- mpr.group_by_geo(df.cluster05)
  df.cluster06.grouped <- mpr.group_by_geo(df.cluster06)
  df.cluster07.grouped <- mpr.group_by_geo(df.cluster07)
  df.cluster08.grouped <- mpr.group_by_geo(df.cluster08)
}

if (!empty_nodes) mpr.draw_map(spain, df.cluster01.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster02.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster03.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster04.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster05.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster06.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster07.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster08.grouped)

Visualización de 10 clústeres:

if (!empty_nodes) {
  plot(hac, hang=-1, labels=F)
  rect.hclust(hac, k=10)
}

Visualización de los clústers en el mapa

A qué clúster pertenece cada nodo del mapa de kohonen:

if (!empty_nodes) {
  groups <- cutree(hac, k=10)
  plot(model, type="mapping",
    bgcol=c("steelblue1","sienna1","yellowgreen","red","blue","yellow","purple","green","white","#1f77b4", '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2')[groups],
    shape = "straight", labels = "")
  add.cluster.boundaries(model, clustering=groups)
}

Análisis de las observaciones de cada cluster

if (!empty_nodes) {
  # Asignamos a cada registro su clúster
  df$cluster <- groups[model$unit.classif]
}

Nuevos dataframes por cluster

if (!empty_nodes) {
  # Creo nuevos dataframes, uno por cada clúster.
  df.cluster01 <- subset(df, cluster==1)
  df.cluster02 <- subset(df, cluster==2)
  df.cluster03 <- subset(df, cluster==3)
  df.cluster04 <- subset(df, cluster==4)
  df.cluster05 <- subset(df, cluster==5)
  df.cluster06 <- subset(df, cluster==6)
  df.cluster07 <- subset(df, cluster==7)
  df.cluster08 <- subset(df, cluster==8)
  df.cluster09 <- subset(df, cluster==9)
  df.cluster10 <- subset(df, cluster==10)

  # Extraigo del dataframe las features.
  df.cluster01 <- select(df.cluster01, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster02 <- select(df.cluster02, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster03 <- select(df.cluster03, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster04 <- select(df.cluster04, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster05 <- select(df.cluster05, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster06 <- select(df.cluster06, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster07 <- select(df.cluster07, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster08 <- select(df.cluster08, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster09 <- select(df.cluster09, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster10 <- select(df.cluster10, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
}

if (!empty_nodes) summary(df.cluster01)

   fecha_cnt           tmax            tmin           precip           nevada 
 Min.   : 1.000   Min.   : 87.0   Min.   : 64.0   Min.   : 0.000   Min.   :0  
 1st Qu.: 3.000   1st Qu.:212.0   1st Qu.:150.0   1st Qu.: 0.000   1st Qu.:0  
 Median : 6.000   Median :233.0   Median :169.0   Median : 1.000   Median :0  
 Mean   : 6.478   Mean   :234.1   Mean   :169.6   Mean   : 5.838   Mean   :0  
 3rd Qu.: 9.000   3rd Qu.:259.0   3rd Qu.:196.0   3rd Qu.: 7.000   3rd Qu.:0  
 Max.   :12.000   Max.   :356.0   Max.   :244.0   Max.   :73.000   Max.   :0  
   prof_nieve          longitud        latitud          altitud     
 Min.   :0.000000   Min.   :27.82   Min.   :-17.89   Min.   : 14.0  
 1st Qu.:0.000000   1st Qu.:28.05   1st Qu.:-16.56   1st Qu.: 25.0  
 Median :0.000000   Median :28.46   Median :-16.26   Median : 33.0  
 Mean   :0.000354   Mean   :28.38   Mean   :-15.95   Mean   :129.9  
 3rd Qu.:0.000000   3rd Qu.:28.48   3rd Qu.:-15.39   3rd Qu.: 64.0  
 Max.   :2.000000   Max.   :28.95   Max.   :-13.60   Max.   :632.0  

if (!empty_nodes) summary(df.cluster02)

   fecha_cnt           tmax            tmin           precip           nevada 
 Min.   : 1.000   Min.   : 95.0   Min.   :-17.0   Min.   : 0.000   Min.   :0  
 1st Qu.: 4.000   1st Qu.:182.0   1st Qu.: 93.0   1st Qu.: 0.000   1st Qu.:0  
 Median : 6.000   Median :227.0   Median :132.0   Median : 5.000   Median :0  
 Mean   : 6.436   Mean   :234.4   Mean   :132.1   Mean   : 9.398   Mean   :0  
 3rd Qu.: 9.000   3rd Qu.:281.0   3rd Qu.:175.0   3rd Qu.:16.000   3rd Qu.:0  
 Max.   :12.000   Max.   :403.0   Max.   :246.0   Max.   :42.000   Max.   :0  
   prof_nieve          longitud        latitud          altitud      
 Min.   : 0.00000   Min.   :35.28   Min.   :-6.949   Min.   :  1.00  
 1st Qu.: 0.00000   1st Qu.:36.50   1st Qu.:-6.332   1st Qu.: 19.00  
 Median : 0.00000   Median :36.83   Median :-5.616   Median : 32.00  
 Mean   : 0.01392   Mean   :36.87   Mean   :-5.396   Mean   : 55.37  
 3rd Qu.: 0.00000   3rd Qu.:37.26   3rd Qu.:-4.846   3rd Qu.: 87.00  
 Max.   :34.00000   Max.   :38.88   Max.   :-1.229   Max.   :200.00  

if (!empty_nodes) summary(df.cluster03)

   fecha_cnt          tmax            tmin           precip     
 Min.   : 1.00   Min.   :  8.0   Min.   :-68.0   Min.   : 0.00  
 1st Qu.: 3.00   1st Qu.:163.0   1st Qu.: 63.0   1st Qu.: 3.00  
 Median : 6.00   Median :209.0   Median :105.0   Median : 8.00  
 Mean   : 6.45   Mean   :213.6   Mean   :109.6   Mean   :10.32  
 3rd Qu.: 9.00   3rd Qu.:269.0   3rd Qu.:160.0   3rd Qu.:15.00  
 Max.   :12.00   Max.   :386.0   Max.   :254.0   Max.   :48.00  
     nevada           prof_nieve          longitud        latitud       
 Min.   :0.000000   Min.   : 0.00000   Min.   :36.85   Min.   :-3.8314  
 1st Qu.:0.000000   1st Qu.: 0.00000   1st Qu.:39.49   1st Qu.: 0.0406  
 Median :0.000000   Median : 0.00000   Median :41.29   Median : 0.6181  
 Mean   :0.000395   Mean   : 0.02423   Mean   :40.72   Mean   : 0.6246  
 3rd Qu.:0.000000   3rd Qu.: 0.00000   3rd Qu.:41.83   3rd Qu.: 1.8744  
 Max.   :4.000000   Max.   :59.00000   Max.   :43.49   Max.   : 4.2156  
    altitud     
 Min.   :  1.0  
 1st Qu.: 29.0  
 Median : 81.0  
 Mean   :172.4  
 3rd Qu.:263.0  
 Max.   :953.0  

if (!empty_nodes) summary(df.cluster04)

   fecha_cnt           tmax            tmin            precip          nevada 
 Min.   : 1.000   Min.   : 42.0   Min.   :-42.00   Min.   : 42.0   Min.   :0  
 1st Qu.: 3.000   1st Qu.:137.0   1st Qu.: 63.00   1st Qu.: 52.0   1st Qu.:0  
 Median : 9.000   Median :165.0   Median : 88.00   Median : 61.0   Median :0  
 Mean   : 7.068   Mean   :169.6   Mean   : 91.69   Mean   : 63.1   Mean   :0  
 3rd Qu.:11.000   3rd Qu.:199.0   3rd Qu.:119.00   3rd Qu.: 73.0   3rd Qu.:0  
 Max.   :12.000   Max.   :352.0   Max.   :219.00   Max.   :112.0   Max.   :0  
   prof_nieve          longitud        latitud            altitud      
 Min.   : 0.00000   Min.   :27.82   Min.   :-17.8889   Min.   :   1.0  
 1st Qu.: 0.00000   1st Qu.:39.48   1st Qu.: -6.0442   1st Qu.:  27.1  
 Median : 0.00000   Median :42.24   Median : -2.9056   Median :  90.0  
 Mean   : 0.05426   Mean   :41.09   Mean   : -3.2526   Mean   : 182.8  
 3rd Qu.: 0.00000   3rd Qu.:43.31   3rd Qu.:  0.0714   3rd Qu.: 261.0  
 Max.   :35.00000   Max.   :43.57   Max.   :  4.2156   Max.   :1405.0  

if (!empty_nodes) summary(df.cluster05)

   fecha_cnt           tmax            tmin            precip           nevada 
 Min.   : 1.000   Min.   : 13.0   Min.   :-33.00   Min.   :  0.00   Min.   :0  
 1st Qu.: 4.000   1st Qu.: 94.0   1st Qu.: 23.00   1st Qu.:  0.00   1st Qu.:0  
 Median : 7.000   Median :129.0   Median : 50.50   Median :  3.00   Median :0  
 Mean   : 6.588   Mean   :138.4   Mean   : 60.82   Mean   : 10.39   Mean   :0  
 3rd Qu.:10.000   3rd Qu.:183.0   3rd Qu.: 98.00   3rd Qu.: 13.00   3rd Qu.:0  
 Max.   :12.000   Max.   :253.0   Max.   :159.00   Max.   :130.00   Max.   :0  
   prof_nieve         longitud        latitud         altitud    
 Min.   : 0.0000   Min.   :28.31   Min.   :-16.5   Min.   :2371  
 1st Qu.: 0.0000   1st Qu.:28.31   1st Qu.:-16.5   1st Qu.:2371  
 Median : 0.0000   Median :28.31   Median :-16.5   Median :2371  
 Mean   : 0.1765   Mean   :28.31   Mean   :-16.5   Mean   :2371  
 3rd Qu.: 0.0000   3rd Qu.:28.31   3rd Qu.:-16.5   3rd Qu.:2371  
 Max.   :46.0000   Max.   :28.31   Max.   :-16.5   Max.   :2371  

if (!empty_nodes) summary(df.cluster06)

   fecha_cnt           tmax            tmin             precip     
 Min.   : 1.000   Min.   : -4.0   Min.   :-110.00   Min.   : 0.00  
 1st Qu.: 4.000   1st Qu.:123.0   1st Qu.:  23.00   1st Qu.: 3.00  
 Median : 6.000   Median :183.0   Median :  66.00   Median : 9.00  
 Mean   : 6.484   Mean   :191.8   Mean   :  71.34   Mean   :12.15  
 3rd Qu.: 9.000   3rd Qu.:261.0   3rd Qu.: 117.00   3rd Qu.:18.00  
 Max.   :12.000   Max.   :400.0   Max.   : 250.00   Max.   :65.00  
     nevada           prof_nieve          longitud        latitud      
 Min.   :0.000000   Min.   :  0.0000   Min.   :37.13   Min.   :-6.600  
 1st Qu.:0.000000   1st Qu.:  0.0000   1st Qu.:40.07   1st Qu.:-4.680  
 Median :0.000000   Median :  0.0000   Median :40.70   Median :-3.764  
 Mean   :0.000498   Mean   :  0.1203   Mean   :40.67   Mean   :-3.352  
 3rd Qu.:0.000000   3rd Qu.:  0.0000   3rd Qu.:41.65   3rd Qu.:-2.138  
 Max.   :6.000000   Max.   :229.0000   Max.   :42.70   Max.   : 2.438  
    altitud      
 Min.   : 405.0  
 1st Qu.: 627.0  
 Median : 735.0  
 Mean   : 777.2  
 3rd Qu.: 916.0  
 Max.   :1894.0  

if (!empty_nodes) summary(df.cluster07)

   fecha_cnt           tmax            tmin            precip          nevada 
 Min.   : 1.000   Min.   : -8.0   Min.   :-58.00   Min.   :158.0   Min.   :0  
 1st Qu.: 2.000   1st Qu.:109.0   1st Qu.: 47.00   1st Qu.:171.0   1st Qu.:0  
 Median : 9.000   Median :129.0   Median : 72.00   Median :186.0   Median :0  
 Mean   : 7.207   Mean   :137.7   Mean   : 74.05   Mean   :203.4   Mean   :0  
 3rd Qu.:11.000   3rd Qu.:152.5   3rd Qu.: 92.00   3rd Qu.:220.5   3rd Qu.:0  
 Max.   :12.000   Max.   :350.0   Max.   :223.00   Max.   :422.0   Max.   :0  
   prof_nieve        longitud        latitud           altitud      
 Min.   :  0.00   Min.   :28.31   Min.   :-16.499   Min.   :   4.0  
 1st Qu.:  0.00   1st Qu.:40.78   1st Qu.: -8.624   1st Qu.:  95.0  
 Median :  0.00   Median :42.24   Median : -8.411   Median : 263.0  
 Mean   :  8.23   Mean   :40.70   Mean   : -6.597   Mean   : 566.3  
 3rd Qu.:  0.00   3rd Qu.:42.89   3rd Qu.: -4.010   3rd Qu.: 370.0  
 Max.   :607.00   Max.   :43.36   Max.   :  2.825   Max.   :2400.0  

if (!empty_nodes) summary(df.cluster08)

   fecha_cnt           tmax            tmin            precip          nevada 
 Min.   : 1.000   Min.   :-42.0   Min.   :-102.0   Min.   : 89.0   Min.   :0  
 1st Qu.: 3.000   1st Qu.:121.0   1st Qu.:  57.0   1st Qu.:102.0   1st Qu.:0  
 Median :10.000   Median :145.0   Median :  80.0   Median :111.0   Median :0  
 Mean   : 7.601   Mean   :149.1   Mean   :  80.7   Mean   :115.1   Mean   :0  
 3rd Qu.:11.000   3rd Qu.:175.5   3rd Qu.: 107.0   3rd Qu.:126.0   3rd Qu.:0  
 Max.   :12.000   Max.   :326.0   Max.   : 211.0   Max.   :163.0   Max.   :0  
   prof_nieve         longitud        latitud           altitud      
 Min.   :  0.000   Min.   :27.82   Min.   :-17.889   Min.   :   1.0  
 1st Qu.:  0.000   1st Qu.:40.80   1st Qu.: -8.411   1st Qu.:  32.0  
 Median :  0.000   Median :42.43   Median : -5.598   Median : 143.0  
 Mean   :  2.786   Mean   :41.45   Mean   : -4.897   Mean   : 310.9  
 3rd Qu.:  0.000   3rd Qu.:43.30   3rd Qu.: -2.039   3rd Qu.: 370.0  
 Max.   :892.000   Max.   :43.57   Max.   :  3.035   Max.   :2371.0  

if (!empty_nodes) summary(df.cluster09)

   fecha_cnt           tmax            tmin            precip     
 Min.   : 1.000   Min.   :  7.0   Min.   :-62.00   Min.   : 0.00  
 1st Qu.: 4.000   1st Qu.:143.0   1st Qu.: 65.00   1st Qu.:16.00  
 Median : 6.000   Median :179.0   Median : 96.00   Median :26.00  
 Mean   : 6.419   Mean   :180.8   Mean   : 96.97   Mean   :26.18  
 3rd Qu.: 9.000   3rd Qu.:219.0   3rd Qu.:133.00   3rd Qu.:36.00  
 Max.   :12.000   Max.   :340.0   Max.   :202.00   Max.   :59.00  
     nevada            prof_nieve          longitud        latitud      
 Min.   :0.0000000   Min.   : 0.00000   Min.   :39.47   Min.   :-8.649  
 1st Qu.:0.0000000   1st Qu.: 0.00000   1st Qu.:42.85   1st Qu.:-8.411  
 Median :0.0000000   Median : 0.00000   Median :43.31   Median :-5.873  
 Mean   :0.0001935   Mean   : 0.05853   Mean   :43.11   Mean   :-5.845  
 3rd Qu.:0.0000000   3rd Qu.: 0.00000   3rd Qu.:43.43   3rd Qu.:-3.819  
 Max.   :2.0000000   Max.   :40.00000   Max.   :43.57   Max.   :-1.008  
    altitud     
 Min.   :  4.0  
 1st Qu.: 42.0  
 Median :108.0  
 Mean   :176.5  
 3rd Qu.:336.0  
 Max.   :656.0  

if (!empty_nodes) summary(df.cluster10)

   fecha_cnt           tmax             tmin             precip      
 Min.   : 1.000   Min.   :-53.00   Min.   :-121.00   Min.   :  0.00  
 1st Qu.: 4.000   1st Qu.: 29.00   1st Qu.: -33.00   1st Qu.: 19.00  
 Median : 6.000   Median : 72.00   Median :   6.00   Median : 30.00  
 Mean   : 6.482   Mean   : 80.96   Mean   :  11.65   Mean   : 34.35  
 3rd Qu.:10.000   3rd Qu.:131.00   3rd Qu.:  56.25   3rd Qu.: 47.00  
 Max.   :12.000   Max.   :263.00   Max.   : 163.00   Max.   :122.00  
     nevada    prof_nieve         longitud        latitud           altitud    
 Min.   :0   Min.   :   0.00   Min.   :40.78   Min.   :-4.0103   Min.   :1572  
 1st Qu.:0   1st Qu.:   0.00   1st Qu.:40.78   1st Qu.:-4.0103   1st Qu.:1894  
 Median :0   Median :   0.00   Median :42.38   Median : 0.9844   Median :2228  
 Mean   :0   Mean   :  13.97   Mean   :41.99   Mean   :-0.1576   Mean   :2145  
 3rd Qu.:0   3rd Qu.:   0.00   3rd Qu.:42.53   3rd Qu.: 1.5242   3rd Qu.:2316  
 Max.   :0   Max.   :1834.00   Max.   :42.77   Max.   : 2.4378   Max.   :2535  

Número de elementos en cada clúster

if (!empty_nodes) {
  df.clusters.dim <- c(dim(df.cluster01)[1], dim(df.cluster02)[1], dim(df.cluster03)[1], dim(df.cluster04)[1], dim(df.cluster05)[1], dim(df.cluster06)[1], dim(df.cluster07)[1], dim(df.cluster08)[1], dim(df.cluster09)[1], dim(df.cluster10)[1])
  barplot(df.clusters.dim,
          names.arg = c("cluster01", "cluster02", "cluster03", "cluster04", "cluster05", "cluster06", "cluster07", "cluster08", "cluster09", "cluster10"),
          col = "steelblue1")
}

Distribución de los datos

if (!empty_nodes) mpr.hist(df.cluster01)

if (!empty_nodes) mpr.hist(df.cluster02)

if (!empty_nodes) mpr.hist(df.cluster03)

if (!empty_nodes) mpr.hist(df.cluster04)

if (!empty_nodes) mpr.hist(df.cluster05)

if (!empty_nodes) mpr.hist(df.cluster06)

if (!empty_nodes) mpr.hist(df.cluster07)

if (!empty_nodes) mpr.hist(df.cluster08)

if (!empty_nodes) mpr.hist(df.cluster09)

if (!empty_nodes) mpr.hist(df.cluster10)

if (!empty_nodes) mpr.boxplot(df.cluster01)
if (!empty_nodes) mpr.boxplot(df.cluster02)

if (!empty_nodes) mpr.boxplot(df.cluster03)

if (!empty_nodes) mpr.boxplot(df.cluster04)

if (!empty_nodes) mpr.boxplot(df.cluster05)

if (!empty_nodes) mpr.boxplot(df.cluster06)

if (!empty_nodes) mpr.boxplot(df.cluster07)

if (!empty_nodes) mpr.boxplot(df.cluster08)

if (!empty_nodes) mpr.boxplot(df.cluster09)

if (!empty_nodes) mpr.boxplot(df.cluster10)

Localización geográfica de las estaciones de medida de cada cluster

# Agrupa por longitud y latitud para rellenar el mapa con menos datos.
if (!empty_nodes) {
  df.cluster01.grouped <- mpr.group_by_geo(df.cluster01)
  df.cluster02.grouped <- mpr.group_by_geo(df.cluster02)
  df.cluster03.grouped <- mpr.group_by_geo(df.cluster03)
  df.cluster04.grouped <- mpr.group_by_geo(df.cluster04)
  df.cluster05.grouped <- mpr.group_by_geo(df.cluster05)
  df.cluster06.grouped <- mpr.group_by_geo(df.cluster06)
  df.cluster07.grouped <- mpr.group_by_geo(df.cluster07)
  df.cluster08.grouped <- mpr.group_by_geo(df.cluster08)
  df.cluster09.grouped <- mpr.group_by_geo(df.cluster09)
  df.cluster10.grouped <- mpr.group_by_geo(df.cluster10)
}

if (!empty_nodes) mpr.draw_map(spain, df.cluster01.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster02.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster03.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster04.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster05.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster06.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster07.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster08.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster09.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster10.grouped)